论文标题
低 - bitrates高动运动视频的主观和客观质量评估
Subjective and Objective Quality Assessment of High-Motion Sports Videos at Low-Bitrates
论文作者
论文摘要
由于自适应比特率流中的网络连接差，通常必须在低比特率上传输和存储视频。因此，设计最佳的比特级梯子，以选择感知优化的分辨率，帧速率和压缩级别的低bItrate视频，以跨Internet进行自适应流式流媒体，这是一项非常有趣的任务。为此，我们对现场体育现场体育的中等和低二核视频进行了首次大规模研究，该视频针对两个编解码器（Elemental AVC和HEVC），并创建了Amazon Prime Video Low-Bitrate Sports（APV LBS）数据集。该研究涉及94名参与者和742个视频，共收集了23,000多个人类意见分数。我们分析了获得的数据，还对客观视频质量评估（VQA）算法进行了广泛的评估，并根据其性能进行了基准测试，并就比特阶梯设计提出了建议。我们正在https://github.com/joshuaebenezer/lbmfr-public提供元数据和VQA功能。
Videos often have to be transmitted and stored at low bitrates due to poor network connectivity during adaptive bitrate streaming. Designing optimal bitrate ladders that would select the perceptually-optimized resolution, frame-rate, and compression level for low-bitrate videos for adaptive streaming across the internet is therefore a task of great interest. Towards that end, we conducted the first large-scale study of medium and low-bitrate videos from live sports for two codecs (Elemental AVC and HEVC) and created the Amazon Prime Video Low-Bitrate Sports (APV LBS) dataset. The study involved 94 participants and 742 videos, with more than 23,000 human opinion scores collected in total. We analyzed the data obtained and we also conducted an extensive evaluation of objective Video Quality Assessment (VQA) algorithms and benchmarked their performance, and make recommendations on bitrate ladder design. We're making the metadata and VQA features available at https://github.com/JoshuaEbenezer/lbmfr-public.
